A Hilbert curve based representation of sEMG signals for gesture recognition

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4 Citations (Scopus)


Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed towards surface electromyography (sEMG) based gesture recognition, often addressed as an image classification problem using Convolutional Neural Networks (CNN). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals that are then classified by CNN. The proposed method is evaluated on different network architectures and yields a classification improvement of more than 3%.

Original languageEnglish
Title of host publicationProceedings of IWSSIP 2019 - 2019 International Conference on Systems, Signals and Image Processing
EditorsSnjezana Rimac-Drlje, Drago Zagar, Irena Galic, Goran Martinovic, Denis Vranjes, Marija Habijan
Number of pages6
ISBN (Electronic)978-1-7281-3227-3
ISBN (Print)978-1-7281-3253-2
Publication statusPublished - Jun 2019

Publication series

NameInternational Conference on Systems, Signals, and Image Processing
ISSN (Print)2157-8672
ISSN (Electronic)2157-8702


  • Hilbert curve
  • hand gesture recognition
  • sEMG
  • electromyography
  • classification
  • CNN
  • Deep Learning

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